This invention relates generally to computer generated recommendations for consumers and, in particular, to generating consumer recommendations based on location data and transaction data.
Advertisers and marketers today often target consumers with marketing messages containing information about products and services, where the messages are tailored for those consumers based on their purchasing patterns. For example, a retailer may track the purchases made by a consumer and may predict products that are likely to be bought by the consumer in the future based on this data. Coupons for these predicted products may be sent in an email message to the consumer to entice them to buy more products from the retailer. Such targeted marketing can be effective for some classes of products since consumer purchasing patterns may be predictable for those classes of products. However, these sorts of marketing techniques are less effective for some classes of products and services because consumer interest in related products and services may be very time and location sensitive. For example, when consumers dine at a restaurant they may often be in the market for a movie afterwards. However, sending a movie ticket through email to the consumers may be ineffective because, first, a coupon for a movie theater may not be of interest to the consumer unless that movie theater is proximate to the restaurant. Second, a coupon for a movie ticket sent through email to the consumer may not be seen by that consumer until later (say the next day), in which case the coupon would not be effective in enticing the consumer to a specific theater.
Thus, there is a need for a technology that can deliver product and service recommendations within time and location constraints that provide relevance to the consumer.